app.py

import asyncio
import platform
import cv2
import torch
import gradio as gr
import numpy as np
import os
import json
import logging
import matplotlib.pyplot as plt
from datetime import datetime
from collections import Counter
from typing import List, Dict, Any, Optional
from ultralytics import YOLO
import ultralytics
import time
import exiftool
import csv

# Set YOLO config directory
os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"

# Set up logging
logging.basicConfig(
    filename="drone_app.log",
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)

# Directories
CAPTURED_FRAMES_DIR = "captured_frames"
OUTPUT_DIR = "outputs"
FLIGHT_LOG_DIR = "flight_logs"
os.makedirs(CAPTURED_FRAMES_DIR, exist_ok=True)
os.makedirs(OUTPUT_DIR, exist_ok=True)
os.makedirs(FLIGHT_LOG_DIR, exist_ok=True)
os.chmod(CAPTURED_FRAMES_DIR, 0o777)
os.chmod(OUTPUT_DIR, 0o777)
os.chmod(FLIGHT_LOG_DIR, 0o777)

# Global variables
log_entries: List[str] = []
detected_counts: List[int] = []
detected_issues: List[str] = []
gps_coordinates: List[List[float]] = []
last_metrics: Dict[str, Any] = {}
frame_count: int = 0
SAVE_IMAGE_INTERVAL = 1  # Save every frame with detections

# SOP Parameters from Annexure-I
DRONE_SPEED_MS = 5  # 5 m/s (18 km/hr)
MIN_SATELLITES = 12
IMAGE_OVERLAP = 0.85  # 85% front and side overlap
MIN_RESOLUTION_MP = 12  # Minimum 12 MP
RECORDING_ANGLE = 90  # Nadir (90 degrees)
IMAGE_FORMAT = "JPEG"

# Annexure-III Operations and Maintenance parameters
DETECTION_CLASSES = [
    "Potholes", "Edge Drops", "Crack", "Raveling", "Rain Cut Embankments",
    "Authorized Median Opening", "Unauthorized Median Opening",
    "Intersection/Crossroads", "Temporary Encroachments", "Permanent Encroachments",
    "Missing Lane Markings", "Missing Boundary Wall", "Damaged Boundary Wall",
    "Open Drain", "Covered Drain", "Blocked Drain", "Unclean Drain",
    "Missing Dissipation Basin"
]

# Debug: Check environment
print(f"Torch version: {torch.__version__}")
print(f"Gradio version: {gr.__version__}")
print(f"Ultralytics version: {ultralytics.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")

# Load custom YOLO model
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
model = YOLO('./data/best.pt').to(device)  # Assumes model is trained for all DETECTION_CLASSES
if device == "cuda":
    model.half()  # Use half-precision (FP16)
print(f"Model classes: {model.names}")

def generate_map(gps_coords: List[List[float]], items: List[Dict[str, Any]]) -> str:
    map_path = os.path.join(OUTPUT_DIR, "map_temp.png")
    plt.figure(figsize=(4, 4))
    plt.scatter([x[1] for x in gps_coords], [x[0] for x in gps_coords], c='blue', label='GPS Points')
    plt.title("Issue Locations Map")
    plt.xlabel("Longitude")
    plt.ylabel("Latitude")
    plt.legend()
    plt.savefig(map_path)
    plt.close()
    return map_path

def write_geotag(image_path: str, gps_coord: List[float]) -> bool:
    try:
        with exiftool.ExifToolHelper() as et:
            et.set_tags(
                [image_path],
                {
                    "EXIF:GPSLatitude": gps_coord[0],
                    "EXIF:GPSLongitude": gps_coord[1],
                    "EXIF:GPSLatitudeRef": "N" if gps_coord[0] >= 0 else "S",
                    "EXIF:GPSLongitudeRef": "E" if gps_coord[1] >= 0 else "W"
                }
            )
        return True
    except Exception as e:
        logging.error(f"Failed to geotag {image_path}: {str(e)}")
        return False

def write_flight_log(frame_count: int, gps_coord: List[float], timestamp: str) -> str:
    log_path = os.path.join(FLIGHT_LOG_DIR, f"flight_log_{frame_count}.csv")
    with open(log_path, 'w', newline='') as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow(["Frame", "Timestamp", "Latitude", "Longitude", "Speed_ms", "Satellites", "Altitude_m"])
        writer.writerow([frame_count, timestamp, gps_coord[0], gps_coord[1], DRONE_SPEED_MS, MIN_SATELLITES, 60])  # Example altitude
    return log_path

def check_sop_compliance(frame: np.ndarray, gps_coord: List[float], frame_count: int) -> bool:
    height, width, _ = frame.shape
    if width * height < MIN_RESOLUTION_MP * 1e6:  # Check resolution (12MP)
        log_entries.append(f"Frame {frame_count}: Resolution below {MIN_RESOLUTION_MP}MP")
        return False
    if len(gps_coord) != 2 or not all(isinstance(x, float) for x in gps_coord):
        log_entries.append(f"Frame {frame_count}: Invalid GPS coordinates")
        return False
    return True

def update_metrics(detections: List[Dict[str, Any]]) -> Dict[str, Any]:
    counts = Counter([det["label"] for det in detections])
    metrics = {
        "items": [{"type": k, "count": v} for k, v in counts.items()],
        "total_detections": len(detections),
        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "sop_compliance": {
            "drone_speed_ms": DRONE_SPEED_MS,
            "image_overlap": IMAGE_OVERLAP,
            "min_resolution_mp": MIN_RESOLUTION_MP,
            "recording_angle_degrees": RECORDING_ANGLE,
            "image_format": IMAGE_FORMAT
        }
    }
    return metrics

def generate_line_chart() -> Optional[str]:
    if not detected_counts:
        return None
    plt.figure(figsize=(4, 2))
    plt.plot(detected_counts[-50:], marker='o', color='#FF8C00')
    plt.title("Detections Over Time")
    plt.xlabel("Frame")
    plt.ylabel("Count")
    plt.grid(True)
    plt.tight_layout()
    chart_path = os.path.join(OUTPUT_DIR, "chart_temp.png")
    plt.savefig(chart_path)
    plt.close()
    return chart_path

async def process_video(video, resize_width=320, resize_height=240, frame_skip=5):
    global frame_count, last_metrics, detected_counts, detected_issues, gps_coordinates, log_entries
    frame_count = 0
    detected_counts.clear()
    detected_issues.clear()
    gps_coordinates.clear()
    log_entries.clear()
    last_metrics = {}

    if video is None:
        log_entries.append("Error: No video uploaded")
        logging.error("No video uploaded")
        return "processed_output.mp4", json.dumps({"error": "No video uploaded"}, indent=2), "\n".join(log_entries), [], None, None

    start_time = time.time()
    cap = cv2.VideoCapture(video)
    if not cap.isOpened():
        log_entries.append("Error: Could not open video file")
        logging.error("Could not open video file")
        return "processed_output.mp4", json.dumps({"error": "Could not open video file"}, indent=2), "\n".join(log_entries), [], None, None

    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    expected_duration = total_frames / fps if fps > 0 else 0
    log_entries.append(f"Input video: {frame_width}x{frame_height}, {fps} FPS, {total_frames} frames, {expected_duration:.2f} seconds")
    logging.info(f"Input video: {frame_width}x{frame_height}, {fps} FPS, {total_frames} frames, {expected_duration:.2f} seconds")
    print(f"Input video: {frame_width}x{frame_height}, {fps} FPS, {total_frames} frames, {expected_duration:.2f} seconds")

    out_width, out_height = resize_width, resize_height
    output_path = os.path.join(OUTPUT_DIR, "processed_output.mp4")
    codecs = [('mp4v', '.mp4'), ('MJPG', '.avi'), ('XVID', '.avi')]
    out = None
    for codec, ext in codecs:
        fourcc = cv2.VideoWriter_fourcc(*codec)
        temp_output_path = os.path.join(OUTPUT_DIR, f"processed_output{ext}")
        out = cv2.VideoWriter(temp_output_path, fourcc, fps, (out_width, out_height))
        if out.isOpened():
            output_path = temp_output_path
            log_entries.append(f"Using codec: {codec}, output: {output_path}")
            logging.info(f"Using codec: {codec}, output: {output_path}")
            break
        else:
            log_entries.append(f"Failed to initialize codec: {codec}")
            logging.warning(f"Failed to initialize codec: {codec}")

    if not out or not out.isOpened():
        log_entries.append("Error: All codecs failed to initialize video writer")
        logging.error("All codecs failed to initialize video writer")
        cap.release()
        return "processed_output.mp4", json.dumps({"error": "All codecs failed"}, indent=2), "\n".join(log_entries), [], None, None

    processed_frames = 0
    all_detections = []
    frame_times = []
    detection_frame_count = 0
    output_frame_count = 0
    last_annotated_frame = None
    data_lake_submission = {
        "images": [],
        "flight_logs": [],
        "analytics": []
    }

    while True:
        ret, frame = cap.read()
        if not ret:
            break
        frame_count += 1
        if frame_count % frame_skip != 0:
            continue
        processed_frames += 1
        frame_start = time.time()

        frame = cv2.resize(frame, (out_width, out_height))
        results = model(frame, verbose=False, conf=0.5, iou=0.7)
        annotated_frame = results[0].plot()

        frame_timestamp = frame_count / fps if fps > 0 else 0
        timestamp_str = f"{int(frame_timestamp // 60)}:{int(frame_timestamp % 60):02d}"

        gps_coord = [17.385044 + (frame_count * 0.0001), 78.486671 + (frame_count * 0.0001)]
        if not check_sop_compliance(frame, gps_coord, frame_count):
            log_entries.append(f"Frame {frame_count}: SOP compliance check failed")
            continue

        frame_detections = []
        for detection in results[0].boxes:
            cls = int(detection.cls)
            conf = float(detection.conf)
            box = detection.xyxy[0].cpu().numpy().astype(int).tolist()
            label = model.names[cls]
            if label in DETECTION_CLASSES:
                frame_detections.append({
                    "label": label,
                    "box": box,
                    "conf": conf,
                    "gps": gps_coord,
                    "timestamp": timestamp_str
                })
                log_message = f"Frame {frame_count} at {timestamp_str}: Detected {label} with confidence {conf:.2f}"
                log_entries.append(log_message)
                logging.info(log_message)

        if frame_detections:
            detection_frame_count += 1
            if detection_frame_count % SAVE_IMAGE_INTERVAL == 0:
                captured_frame_path = os.path.join(CAPTURED_FRAMES_DIR, f"detected_{frame_count:06d}.jpg")
                if not cv2.imwrite(captured_frame_path, annotated_frame):
                    log_entries.append(f"Error: Failed to save {captured_frame_path}")
                    logging.error(f"Failed to save {captured_frame_path}")
                else:
                    if write_geotag(captured_frame_path, gps_coord):
                        detected_issues.append(captured_frame_path)
                        data_lake_submission["images"].append({
                            "path": captured_frame_path,
                            "frame": frame_count,
                            "gps": gps_coord,
                            "timestamp": timestamp_str
                        })
                        if len(detected_issues) > 100:
                            detected_issues.pop(0)
                    else:
                        log_entries.append(f"Error: Failed to geotag {captured_frame_path}")

        flight_log_path = write_flight_log(frame_count, gps_coord, timestamp_str)
        data_lake_submission["flight_logs"].append({
            "path": flight_log_path,
            "frame": frame_count
        })

        out.write(annotated_frame)
        output_frame_count += 1
        last_annotated_frame = annotated_frame
        if frame_skip > 1:
            for _ in range(frame_skip - 1):
                out.write(annotated_frame)
                output_frame_count += 1

        detected_counts.append(len(frame_detections))
        gps_coordinates.append(gps_coord)
        all_detections.extend(frame_detections)

        detection_summary = {
            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "video_timestamp": timestamp_str,
            "frame": frame_count,
            "gps": gps_coord,
            "processing_time_ms": (time.time() - frame_start) * 1000,
            "detections": {label: sum(1 for det in frame_detections if det["label"] == label) for label in DETECTION_CLASSES}
        }
        data_lake_submission["analytics"].append(detection_summary)
        log_entries.append(json.dumps(detection_summary, indent=2))
        if len(log_entries) > 50:
            log_entries.pop(0)

    while output_frame_count < total_frames and last_annotated_frame is not None:
        out.write(last_annotated_frame)
        output_frame_count += 1

    last_metrics = update_metrics(all_detections)
    data_lake_submission["metrics"] = last_metrics
    data_lake_submission["frame_count"] = frame_count
    data_lake_submission["gps_coordinates"] = gps_coordinates[-1] if gps_coordinates else [0, 0]

    submission_json_path = os.path.join(OUTPUT_DIR, "data_lake_submission.json")
    with open(submission_json_path, 'w') as f:
        json.dump(data_lake_submission, f, indent=2)

    cap.release()
    out.release()

    cap = cv2.VideoCapture(output_path)
    output_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    output_fps = cap.get(cv2.CAP_PROP_FPS)
    output_duration = output_frames / output_fps if output_fps > 0 else 0
    cap.release()

    total_time = time.time() - start_time
    avg_frame_time = sum(frame_times) / len(frame_times) if frame_times else 0
    log_entries.append(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds")
    log_entries.append(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}")
    logging.info(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds")
    logging.info(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}")
    print(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds")
    print(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}")

    chart_path = generate_line_chart()
    map_path = generate_map(gps_coordinates[-5:], all_detections)

    return (
        output_path,
        json.dumps(last_metrics, indent=2),
        "\n".join(log_entries[-10:]),
        detected_issues,
        chart_path,
        map_path
    )

# Gradio interface
with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange")) as iface:
    gr.Markdown("# NHAI Drone Analytics Dashboard")
    with gr.Row():
        with gr.Column(scale=3):
            video_input = gr.Video(label="Upload Drone Video")
            width_slider = gr.Slider(320, 640, value=320, label="Output Width", step=1)
            height_slider = gr.Slider(240, 480, value=240, label="Output Height", step=1)
            skip_slider = gr.Slider(1, 10, value=5, label="Frame Skip", step=1)
            process_btn = gr.Button("Process Video", variant="primary")
        with gr.Column(scale=1):
            metrics_output = gr.Textbox(label="Detection Metrics", lines=5, interactive=False)
    with gr.Row():
        video_output = gr.Video(label="Processed Video")
        issue_gallery = gr.Gallery(label="Detected Issues", columns=4, height="auto", object_fit="contain")
    with gr.Row():
        chart_output = gr.Image(label="Detection Trend")
        map_output = gr.Image(label="Issue Locations Map")
    with gr.Row():
        logs_output = gr.Textbox(label="Logs", lines=5, interactive=False)

    process_btn.click(
        process_video,
        inputs=[video_input, width_slider, height_slider, skip_slider],
        outputs=[video_output, metrics_output, logs_output, issue_gallery, chart_output, map_output]
    )

if platform.system() == "Emscripten":
    asyncio.ensure_future(process_video())
else:
    if __name__ == "__main__":
        iface.launch()